Cooperators have low degrees
#which model to evaluate
model3 <- model3_no_alterbehavior(redo=TRUE)
#Define degrees of isolation
isolationDegree = 2
#number of iterations per arm
iterations = 100
modelForPrediction = "random forest" #"linear" or "random forest"
# List of manipulating parameters of experiments
#L : number of rounds
#V : Visible or not
#A : Income of a rich-group subject
#B : Income of a poor-group subject
#R : Probability to be assigned to a rich group
#I : Number of the same-parameter trial
R = 0.5
I = 0
L = 10
trends.df = data.frame()
for(A in c(1150,700,500)){
for(V in c(0,1)){
V = V
A = A
if(A==1150){B = 200} #high inequality
if(A==700){B = 300} #low inequality
if(A==500){B = 500} #no inequality
if(modelForPrediction=="random forest"){
if(V==0){
model1<-model1.invisible(redo=FALSE)
model2<-model2.invisible(redo=FALSE)
model3<-model3
}
if(V==1){
model1<-model1.visible(redo=FALSE)
model2<-model2.visible(redo=FALSE)
model3<-model3
}
}
df.netIntLowDegree = data.frame(
coopFrac = NULL,
avgCoop = NULL,
avgCoopFinal = NULL,
percentIsolation = NULL,
isolation = NULL,
percentIsolationC = NULL,
percentIsolationD = NULL,
nCommunities = NULL,
communitySize = NULL,
assortativityInitial = NULL,
assortativityFinal = NULL,
conversionRate = NULL,
conversionToD = NULL,
conversionToC = NULL,
transitivity = NULL,
degree = NULL,
degreeC = NULL,
degreeD = NULL,
meanConversionToD = NULL,
meanConversionToC = NULL,
degreeLost = NULL,
degreeLostC = NULL,
degreeLostD = NULL
)
for(frac in c(0.25,0.5,0.75,1)){
#nodes in the top fractionCoop degrees will automatically be a cooperator
fractionCoop = frac
coopFrac = NULL
avgCoop = NULL
homophilyC = NULL
homophilyD = NULL
heterophily = NULL
avgCoopFinal = NULL
percentIsolation = NULL
isolation = NULL
percentIsolationC = NULL
percentIsolationD = NULL
nCommunities = NULL
communitySize = NULL
assortativityInitial = NULL
assortativityFinal = NULL
conversionRate = NULL
conversionToD = NULL
conversionToC = NULL
transitivity = NULL
degree = NULL
degreeC = NULL
degreeD = NULL
meanConversionToD = NULL
meanConversionToC = NULL
degreeLost = NULL
degreeLostC = NULL
degreeLostD = NULL
avg_wealth = NULL
gini = NULL
for(m in c(1:iterations)){
# Section 1. NOTES, packages, and Parameters
#Importing library
library(igraph) # for network graphing
library(reldist) # for gini calculatio
library(boot) # for inv.logit calculation
#Two prefixed functions
#rank
rank1 = function(x) {rank(x,na.last=NA,ties.method="average")[1]} #a smaller value has a smaller rank.
#gini mean difference (a.k.a. mean difference: please refer to https://stat.ethz.ch/pipermail/r-help/2003-April/032782.html)
gmd = function(x) {
x1 = na.omit(x)
n = length(x1)
tmp = 0
for (i in 1:n) {
for (j in 1:n) {
tmp <- tmp + abs(x1[i]-x1[j])
}
}
answer = tmp/(n*n)
return(answer)
}
# List of fixed parameters of experiments (assumptions)
#Rewiring rate = 0.3
#GINI coefficient (can be known by A or B)
GINI = 0*as.numeric(A==500) + 0.2*as.numeric(A %in% c(700,850)) + 0.4*as.numeric(A ==1150)
#Collecting data frame (final output data frame)
result = data.frame(round=0:L,n_par=NA,n_A=NA,avg_coop=NA,avg_degree=NA,avg_wealth=NA,gini=NA,gmd=NA,avg_coop_A=NA,avg_degree_A=NA,avg_wealth_A=NA,gini_A=NA,gmd_A=NA,avg_coop_B=NA,avg_degree_B=NA,avg_wealth_B=NA,gini_B=NA,gmd_B=NA,isolation=NA,percentIsolation=NA,meanConversionToD=NA,meanConversionToC=NA,degreeLost=NA,degreeLostC=NA,degreeLostD=NA)
#_A is for a richer group and _B is for a poorer group
#####################################################
# Section 1.5: Practice rounds 1 to 2, to determine C/D in round 1
N = 17 # median of the number of participants over rounds.
node_rp0 = data.frame(ego_id=1:N, round=0)
node_import = node_rp0
for (k in 1:2){
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if (k==1) {
node_rX$prob_coop = inv.logit(1.099471)
} else {
node_rX$prob_coop = inv.logit((-0.02339288) + (1.46068980)*as.numeric(node_rX$prev_coop==1))
}
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
node_rX$prev_coop = node_rX$coop
assign(paste("coop_rp",k, sep=""),node_rX$coop)
#For the loop
node_import = node_rX
}
#cooperation rate in the practice rounds
coop_rp = apply(cbind(coop_rp1,coop_rp2),1,mean)
#####################################################
# Section 2: Round 0 (Agents and environments)
#Node data generation
N = 17 # median of the number of participants over rounds.
node_r0 = data.frame(ego_id=1:N, round=0)
node_r0$coop_rp = ifelse(coop_rp==1,"C","D")
node_r0$group = sample(c("rich","poor"),N,replace=TRUE,prob=c(R,1-R)) #R is defined as the probability to be assigned to the rich group
node_r0$initial_wealth = ifelse(node_r0$group=="rich",A,B)
#Link data generation
ego_list = NULL
for (i in 1:N) { ego_list = c(ego_list,rep(i,N)) }
link_r0 = data.frame(ego_id=ego_list,alt_id=rep(1:N,N))
link_r0 = link_r0[(link_r0$ego_id < link_r0$alt_id),] #The link was bidirectional, and thus the half and self are omitted.
link_r0$connected = sample(0:1,dim(link_r0)[1],replace=TRUE,prob=c(0.7,0.3)) #Initial rewiring rate is fixed, 0.3
link_r0c_ego = link_r0[link_r0$connected==1,]
link_r0c_alt = link_r0[link_r0$connected==1,]
colnames(link_r0c_alt) = c("alt_id","ego_id","connected")
link_r0c = rbind(link_r0c_ego,link_r0c_alt) #this is bidirectional (double counted) for connected ties.
link_r0c = link_r0c[order(link_r0c$ego_id),]
link_r0c$alternumber = NA #putting the number for each alter in the same ego
link_r0c[1,]$alternumber = 1
for (i in 1:(dim(link_r0c)[1]-1))
{if (link_r0c[i,]$ego_id == link_r0c[i+1,]$ego_id)
{link_r0c[i+1,]$alternumber = link_r0c[i,]$alternumber + 1}
else
{link_r0c[i+1,]$alternumber = 1}
#print(i)
}
link_r0c2 = reshape(link_r0c, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_r0c2$initial_degree = apply(link_r0c2[,colnames(link_r0c2)[substr(colnames(link_r0c2),1,6) == "alt_id"]],1,function(x){length(na.omit(x))}) #Degree of each ego
link_r0c2[is.na(link_r0c2$initial_degree)==1,"initial_degree"] = 0
#Reflect the degree and initial local gini coefficient into the node data
node_r0 = merge(x=node_r0,y=link_r0c2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_r0$initial_avg_env_wealth = NA
node_r0$initial_local_gini = NA #local gini coefficient of the ego and connecting alters
node_r0$initial_rel_rank = NA #local rank of ego among the ego and connecting alters (divided by the number of the go and connecting alters)
for (i in 1:(dim(node_r0)[1])){
node_r0[i,]$initial_avg_env_wealth = mean(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_local_gini = gini(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_rel_rank = rank1(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))/length(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
}
#Finalization of round 0 and Visualization
#plot(graph.data.frame(link_r0[link_r0$connected==1,],directed=F)) #plot.igraph
node_r0$everIsolated = 0
node_r0$maxDegreeLost = NA
result[result$round==0,2:25] = c(length(node_r0$ego_id),length(node_r0[node_r0$group=="rich",]$ego_id),NA,mean(node_r0$initial_degree),mean(node_r0$initial_wealth),gini(node_r0$initial_wealth),gmd(node_r0$initial_wealth),NA,mean(node_r0[node_r0$group=="rich",]$initial_degree),mean(node_r0[node_r0$group=="rich",]$initial_wealth),gini(node_r0[node_r0$group=="rich",]$initial_wealth),gmd(node_r0[node_r0$group=="rich",]$initial_wealth),NA,mean(node_r0[node_r0$group=="poor",]$initial_degree),mean(node_r0[node_r0$group=="poor",]$initial_wealth),gini(node_r0[node_r0$group=="poor",]$initial_wealth),gmd(node_r0[node_r0$group=="poor",]$initial_wealth),
as.numeric(ifelse(is.na(table(node_r0$initial_degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_r0$everIsolated)/length(node_r0$ego_id)),
NA,
NA,
NA,NA,NA
)
#For the loop at the next round (for round 1, the initial one is the same as the previous [1 prior] one)
node_import = node_r0
node_import$initial_coop = NA
node_import$prev_coop = NA
node_import$prev_wealth = node_import$initial_wealth
node_import$prev_degree = node_import$initial_degree
node_import$prev_avg_env_wealth = node_import$initial_avg_env_wealth
node_import$prev_local_gini = node_import$initial_local_gini
node_import$prev_rel_rank = node_import$initial_rel_rank
node_import$prev_local_rate_coop = NA
link_import = link_r0
#####################################################
# Section 3: Rounds 1 to 10 or more (behaviors in simulation: the equation of cooperation is different at round 1 because of no history)
#3-1: Cooperation phase
for (k in 1:L)
{
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if(modelForPrediction=="linear"){
if (k==1) {
node_rX$prob_coop = as.numeric(V==0)*inv.logit((-1.816665) + (2.086067)*coop_rp1 + (1.800153)*coop_rp2) + as.numeric(V==1)*inv.logit((-2.031577) + (2.427157)*coop_rp1 + (1.684193)*coop_rp2 + (-1.528851)*GINI)
} else {
node_rX$prob_coop = as.numeric(V==0 & node_rX$prev_coop==0)*inv.logit(-1.039916) + as.numeric(V==0 & node_rX$prev_coop==1)*inv.logit(2.062023) + as.numeric(V==1 & node_rX$prev_coop==0)*inv.logit((-0.2574838)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-1.214198)*GINI + (2.508148)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.9749075)) + as.numeric(V==1 & node_rX$prev_coop==1)*inv.logit((- 0.6197254)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.7480261)*GINI + (1.169674)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (1.356784))
}
}
if(modelForPrediction=="random forest"){
if (k==1) {
if(V==1){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2,
gini = GINI
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2
),
type = "prob"
)[[1]]$C}
} else {
if(V==1){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
gini = GINI,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
}
}
#####manipulate cooperation rate ar round 1 depending on the degree! (keep the total coopertion rate at round 1 constant)
if(k==1){
prob_coop_df = NULL
nodesCoop = NULL
#nodesCoop = node_rX$prev_degree<=quantile(node_rX$prev_degree,fractionCoop) #assign low-degree nodes to cooperators
#assign defectors to designated nodes
nodesCoop = node_rX$prev_degree<=floor(quantile(node_rX$prev_degree,fractionCoop)) & node_rX$prev_degree>=floor(quantile(node_rX$prev_degree,fractionCoop-0.25))
prob_coop_df =
data.frame(
prob_coop = rev(node_rX$prob_coop[order(coop_rp)]),
node_number = c(which(!nodesCoop),which(nodesCoop))
)
node_rX$prob_coop = prob_coop_df[order(prob_coop_df$node_number),]$prob_coop
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
} else {
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
if (k==1) {
node_rX$initial_coop = node_rX$coop
} else {
node_rX$initial_coop = node_rX$initial_coop
}
node_rX$cost = (-50)*node_rX$coop*node_rX$prev_degree
node_rX$n_coop_received = NA
for (i in 1:(dim(node_rX)[1]))
{
node_rX[i,]$n_coop_received = sum(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) ==
"alt_id"]],"coop"])
}
node_rX$benefit = 100*node_rX$n_coop_received
node_rX$payoff = node_rX$cost + node_rX$benefit
node_rX$wealth = node_rX$prev_wealth + node_rX$payoff
node_rX$rel_rank = NA
node_rX$local_rate_coop = NA
for (i in 1:dim(node_rX)[1])
{
node_rX[i,]$rel_rank = rank1(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX[node_rX$ego_id %in%
node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX[i,]$local_rate_coop = mean(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"coop"]))
}
node_rX$growth = as.numeric((node_rX$wealth/node_rX$prev_wealth) > 1)
node_rX = node_rX[,c("ego_id","round","group","prev_degree","initial_wealth","initial_local_gini","initial_coop","coop","wealth","rel_rank","local_rate_coop","growth","everIsolated","maxDegreeLost")] #Pruning the previous-round data (degree is not updating yet)
#3-2: Rewiring phase
# 30% of ties (unidirectional) are being rewired
link_rX_1 = link_import #Importing data (bidirectioanl ego-alter [ego_id < alter_id])
colnames(link_rX_1) = c("ego_id","alt_id","prev_connected")
link_rX_1$challenge = sample(0:1,dim(link_rX_1)[1],replace=TRUE,prob=c(0.7,0.3)) # The bidirectional ties being rewired are selected (rewiring rate = 0.3).
ego_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(ego_node_data) =
c("ego_id","ego_wealth","ego_coop","ego_prev_degree","ego_initial_wealth","ego_initial_local_gini","ego_initial_coop","ego_rel_rank","ego_local_rate_coop","ego_growth")
alt_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(alt_node_data) =
c("alt_id","alt_wealth","alt_coop","alt_prev_degree","alt_initial_wealth","alt_initial_local_gini","alt_initial_coop","alt_rel_rank","alt_local_rate_coop","alt_growth")
link_rX_2 = merge(x=link_rX_1,y=ego_node_data,all.x=TRUE,all.y=FALSE,by="ego_id")
link_rX_3 = merge(x=link_rX_2,y=alt_node_data,all.x=TRUE,all.y=FALSE,by="alt_id")
link_rX_3$choice = sample(c("ego","alt"),dim(link_rX_3)[1],replace=TRUE,prob=c(0.5,0.5)) #decision maker for breaking a link, which is a unilateral decision
#ego_prob: probability of choosing to connect when challenged (asked)
if(modelForPrediction=="linear"){
link_rX_3$ego_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$ego_coop + (2.96549)*link_rX_3$alt_coop + (-0.1808545))
link_rX_3$alt_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$alt_coop + (2.96549)*link_rX_3$ego_coop + (-0.1808545))}
if(modelForPrediction=="random forest"){
link_rX_3$ego_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$ego_coop,
alter_behavior = link_rX_3$alt_coop
),
type = "prob"
)[[1]]$C
link_rX_3$alt_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$alt_coop,
alter_behavior = link_rX_3$ego_coop
),
type = "prob"
)[[1]]$C
}
link_rX_3$prob_connect = ifelse(link_rX_3$prev_connected == 1, ifelse(link_rX_3$choice == "ego", link_rX_3$ego_prob,
link_rX_3$alt_prob), link_rX_3$ego_prob*link_rX_3$alt_prob)
link_rX_3$connect_update = apply(data.frame(link_rX_3$prob_connect),1, function(x) {sample(1:0,1,prob=c(x,(1-x)))})
link_rX_3$connected = ifelse(link_rX_3$challenge==0,link_rX_3$prev_connected,link_rX_3$connect_update)
link_rX = link_rX_3[,c("ego_id","alt_id","connected")] #pruning and data is updated
#Reflect the degree and local gini coefficient into the node data
link_rXc_ego = link_rX[link_rX$connected==1,]
link_rXc_alt = link_rX[link_rX$connected==1,]
colnames(link_rXc_alt) = c("alt_id","ego_id","connected")
link_rXc = rbind(link_rXc_ego,link_rXc_alt)
link_rXc = link_rXc[order(link_rXc$ego_id),]
link_rXc$alternumber = NA
link_rXc[1,]$alternumber = 1
for (i in 1:(dim(link_rXc)[1]-1))
{
if (link_rXc[i,]$ego_id == link_rXc[i+1,]$ego_id)
{
link_rXc[i+1,]$alternumber = link_rXc[i,]$alternumber + 1
}
else
{
link_rXc[i+1,]$alternumber = 1
}
#print(i)
}
link_rXc2 = reshape(link_rXc, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_rXc2$degree = apply(link_rXc2[,colnames(link_rXc2)[substr(colnames(link_rXc2),1,3) == "alt"]],1,function(x) {length(na.omit(x))})
node_rX_final = merge(x=node_rX[,c("ego_id","round","group","initial_wealth","initial_local_gini","initial_coop","coop","wealth","growth","everIsolated","maxDegreeLost")],y=link_rXc2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_rX_final[is.na(node_rX_final$degree)==1,"degree"] = 0
node_rX_final$avg_env_wealth = NA
node_rX_final$local_gini = NA #needs to be updated because the social network changes at the rewiring phase
node_rX_final$local_rate_coop = NA
node_rX_final$rel_rank = NA
for (i in 1:dim(node_rX_final)[1])
{
node_rX_final[i,]$avg_env_wealth = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_gini = gini(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_rate_coop = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"coop"]))
node_rX_final[i,]$rel_rank = rank1(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$everIsolated = ifelse(node_rX_final[i,]$everIsolated==1,1,ifelse(node_rX_final[i,]$degree<=isolationDegree,1,0))
node_rX_final[i,]$maxDegreeLost = pmax(node_r0[i,]$initial_degree - node_rX_final[i,]$degree, node_rX_final[i,]$maxDegreeLost, na.rm=TRUE)
}
#Finalization of round X and Visualization
#plot(graph.data.frame(link_rX[link_rX$connected==1,],directed=F)) #plot.igraph
result[result$round==k,2:25] =
c(length(node_rX_final$ego_id),length(node_rX_final[node_rX_final$group=="rich",]$ego_id),mean(node_rX_final$coop),mean(node_rX_final$degree),mean(node_rX_final$wealth),gini(node_rX_final$wealth),gmd(node_rX_final$wealth),mean(node_rX_final[node_rX_final$group=="rich",]$coop),mean(node_rX_final[node_rX_final$group=="rich",]$degree),mean(node_rX_final[node_rX_final$group=="rich",]$wealth),gini(node_rX_final[node_rX_final$group=="rich",]$wealth),gmd(node_rX_final[node_rX_final$group=="rich",]$wealth),mean(node_rX_final[node_rX_final$group=="poor",]$coop),mean(node_rX_final[node_rX_final$group=="poor",]$degree),mean(node_rX_final[node_rX_final$group=="poor",]$wealth),gini(node_rX_final[node_rX_final$group=="poor",]$wealth),gmd(node_rX_final[node_rX_final$group=="poor",]$wealth),
as.numeric(ifelse(is.na(table(node_rX_final$degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_rX_final$everIsolated)/length(node_rX_final$ego_id)),
prop.table(table(node_rX_final[node_rX_final$initial_coop==1]$coop))["0"],
prop.table(table(node_rX_final[node_rX_final$initial_coop==0]$coop))["1"],
suppressWarnings({mean(node_rX_final$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==1]$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==0]$maxDegreeLost,na.rm=TRUE)})
)
#For the loop
node_import = node_rX_final
colnames(node_import)[colnames(node_import) %in%
c("coop","wealth","growth","degree","avg_env_wealth","local_gini","local_rate_coop","rel_rank")] =
c("prev_coop","prev_wealth","prev_growth","prev_degree","prev_avg_env_wealth","prev_local_gini","prev_local_rate_coop","prev_rel_rank")
link_import = link_rX
#print(paste0("Round ",k," is done."))
}
trends.df = rbind(trends.df,cbind(result[c("round","gini","gmd","avg_wealth","avg_coop","avg_degree")],V,GINI,fractionCoop))
link_rX_final = data.table::melt(setDT(node_rX_final),
measure = patterns('alt_id'),
variable.name = 'linkNumber',
value.name = c('alt_id'))
link_rX_final = data.frame(link_rX_final)[c("ego_id","alt_id")]
link_rX_final = link_rX_final[complete.cases(link_rX_final),]
link_rX_final = data.frame(t(unique(apply(link_rX_final, 1, function(x) sort(x))))) %>% distinct(X1, X2)
node_g_final = data.frame(node_rX_final)[c("ego_id","initial_coop","coop")]
node_g_final$initial_coop = factor(node_g_final$initial_coop)
g_rX_final = graph_from_data_frame(link_rX_final, directed = FALSE, vertices=node_g_final)
g_r0 = graph_from_data_frame(link_r0[link_r0$connected==1,][1:2], directed = FALSE, vertices=node_r0)
E(g_r0)$coopEdgeC = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0)))
E(g_r0)$coopEdgeD = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="D",1,0)))
E(g_r0)$coopEdgeCD = sapply(E(g_r0), function(e) ifelse(sum(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0))==1,1,0))
#C-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyC[m] = sum(E(g_r0)$coopEdgeC) / (table(V(g_r0)$coop_rp)["C"]*(table(V(g_r0)$coop_rp)["C"]-1)/2)
#D-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyD[m] = sum(E(g_r0)$coopEdgeD) / (table(V(g_r0)$coop_rp)["D"]*(table(V(g_r0)$coop_rp)["D"]-1)/2)
#heterophily, defined as number of observed C-D edges out of total possible C-D edges
heterophily[m] = sum(E(g_r0)$coopEdgeCD) / (table(V(g_r0)$coop_rp)["C"]*table(V(g_r0)$coop_rp)["D"])
coopFrac[m] = fractionCoop
avgCoop[m] = prop.table(table(V(g_r0)$coop_rp))["C"]
avgCoopFinal[m] = result[result$round==10,]$avg_coop
percentIsolation[m] = max(result[result$round>=1,]$percentIsolation)
isolation[m] = max(result[result$round>=1,]$isolation)
#percentage of isolation among those who cooperated in both practice rounds
percentIsolationC[m] = sum(node_rX_final[coop_rp==1,]$everIsolated)/length(node_rX_final[coop_rp==1,]$everIsolated)
#percentage of isolation among those who defected at least once in practice rounds
percentIsolationD[m] = sum(node_rX_final[coop_rp<=0.5,]$everIsolated)/length(node_rX_final[coop_rp<=0.5,]$everIsolated)
nCommunities[m] = max(membership(cluster_louvain(g_rX_final)),na.rm=TRUE)
communitySize[m] = mean(table(membership(cluster_louvain(g_rX_final))),na.rm=TRUE)
assortativityInitial[m] = assortativity(g_r0, V(g_r0)$coop_rp == "C")
assortativityFinal[m] = assortativity(g_rX_final, V(g_r0)$coop_rp == "C")
conversionRate[m] = prop.table(table(V(g_rX_final)$coop == ifelse(V(g_r0)$coop_rp=="C","1","0")))["FALSE"]
conversionToD[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["0"]
conversionToC[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["1"]
transitivity[m] = mean(transitivity(g_rX_final, type="global"),na.rm=TRUE)
degree[m] = mean(igraph::degree(g_rX_final),na.rm=TRUE)
degreeC[m] = mean(igraph::degree(g_r0)[V(g_r0)$coop_rp=="C"],na.rm=TRUE)
degreeD[m] = mean(igraph::degree(g_r0)[V(g_r0)$coop_rp=="D"],na.rm=TRUE)
meanConversionToD[m] = mean(result[result$round>=2,]$meanConversionToD, na.rm=TRUE)
meanConversionToC[m] = mean(result[result$round>=2,]$meanConversionToC, na.rm=TRUE)
degreeLost[m] = result[result$round==10,]$degreeLost
degreeLostC[m] = result[result$round==10,]$degreeLostC
degreeLostD[m] = result[result$round==10,]$degreeLostD
avg_wealth[m] = result[result$round==10,]$avg_wealth
gini[m] = result[result$round==10,]$gini
}
df.netIntLowDegree = rbind(df.netIntLowDegree,
data.frame(
coopFrac = coopFrac,
avgCoop = avgCoop,
avgCoopFinal = avgCoopFinal,
percentIsolation = percentIsolation,
isolation = isolation,
percentIsolationC = percentIsolationC,
percentIsolationD = percentIsolationD,
nCommunities = nCommunities,
communitySize = communitySize,
assortativityInitial = assortativityInitial,
assortativityFinal = assortativityFinal,
conversionRate = conversionRate,
conversionToD = conversionToD,
conversionToC = conversionToC,
homophilyC = homophilyC,
homophilyD = homophilyD,
heterophily = heterophily,
transitivity = transitivity,
degree = degree,
degreeC = degreeC,
degreeD = degreeD,
meanConversionToD = meanConversionToD,
meanConversionToC = meanConversionToC,
degreeLost = degreeLost,
degreeLostC = degreeLostC,
degreeLostD = degreeLostD,
avg_wealth = avg_wealth,
gini = gini
))
#plot(g_r0,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", round 0",sep=""))
#plot(g_rX_final,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", final round",sep=""))
}
sum.netIntLowDegree <- data.frame(
df.netIntLowDegree %>%
group_by(coopFrac) %>%
summarise(
mean.isolation = mean(isolation),
ci.isolation = 1.96 * sd(isolation)/sqrt(n()),
mean.percentIsolation = mean(percentIsolation),
ci.percentIsolation = 1.96 * sd(percentIsolation)/sqrt(n()),
mean.percentIsolationC = mean(percentIsolationC,na.rm=TRUE),
ci.percentIsolationC = 1.96 * sd(percentIsolationC,na.rm=TRUE)/sqrt(sum(isolation)),
mean.percentIsolationD = mean(percentIsolationD,na.rm=TRUE),
ci.percentIsolationD = 1.96 * sd(percentIsolationD,na.rm=TRUE)/sqrt(sum(isolation)),
mean.avgCoop = mean(avgCoop,na.rm=TRUE),
ci.avgCoop = 1.96 * sd(avgCoop,na.rm=TRUE)/sqrt(n()),
mean.avgCoopFinal = mean(avgCoopFinal,na.rm=TRUE),
ci.avgCoopFinal = 1.96 * sd(avgCoopFinal,na.rm=TRUE)/sqrt(n()),
mean.nCommunities = mean(nCommunities,na.rm=TRUE),
ci.nCommunities = 1.96 * sd(nCommunities,na.rm=TRUE)/sqrt(n()),
mean.communitySize = mean(communitySize,na.rm=TRUE),
ci.communitySize = 1.96 * sd(communitySize,na.rm=TRUE)/sqrt(n()),
mean.assortativityInitial = mean(assortativityInitial,na.rm=TRUE),
ci.assortativityInitial = 1.96 * sd(assortativityInitial,na.rm=TRUE)/sqrt(n()),
mean.assortativityFinal = mean(assortativityFinal,na.rm=TRUE),
ci.assortativityFinal = 1.96 * sd(assortativityFinal,na.rm=TRUE)/sqrt(n()),
mean.conversionRate = mean(conversionRate,na.rm=TRUE),
ci.conversionRate = 1.96 * sd(conversionRate,na.rm=TRUE)/sqrt(n()),
mean.conversionToD = mean(conversionToD,na.rm=TRUE),
ci.conversionToD = 1.96 * sd(conversionToD,na.rm=TRUE)/sqrt(n()),
mean.conversionToC = mean(conversionToC,na.rm=TRUE),
ci.conversionToC = 1.96 * sd(conversionToC,na.rm=TRUE)/sqrt(n()),
mean.homophilyC = mean(homophilyC,na.rm=TRUE),
ci.homophilyC = 1.96 * sd(homophilyC,na.rm=TRUE)/sqrt(n()),
mean.homophilyD = mean(homophilyD,na.rm=TRUE),
ci.homophilyD = 1.96 * sd(homophilyD,na.rm=TRUE)/sqrt(n()),
mean.heterophily = mean(heterophily,na.rm=TRUE),
ci.heterophily = 1.96 * sd(heterophily,na.rm=TRUE)/sqrt(n()),
mean.transitivity = mean(transitivity,na.rm=TRUE),
ci.transitivity = 1.96 * sd(transitivity,na.rm=TRUE)/sqrt(n()),
mean.degree = mean(degree,na.rm=TRUE),
ci.degree = 1.96 * sd(degree,na.rm=TRUE)/sqrt(n()),
mean.degreeC = mean(degreeC,na.rm=TRUE),
ci.degreeC = 1.96 * sd(degreeC,na.rm=TRUE)/sqrt(n()),
mean.degreeD = mean(degreeD,na.rm=TRUE),
ci.degreeD = 1.96 * sd(degreeD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToD = mean(meanConversionToD,na.rm=TRUE),
ci.meanConversionToD = 1.96 * sd(meanConversionToD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToC = mean(meanConversionToC,na.rm=TRUE),
ci.meanConversionToC = 1.96 * sd(meanConversionToC,na.rm=TRUE)/sqrt(n()),
mean.degreeLost = mean(degreeLost,na.rm=TRUE),
ci.degreeLost = 1.96 * sd(degreeLost,na.rm=TRUE)/sqrt(n()),
mean.degreeLostC = mean(degreeLostC,na.rm=TRUE),
ci.degreeLostC = 1.96 * sd(degreeLostC,na.rm=TRUE)/sqrt(n()),
mean.degreeLostD = mean(degreeLostD,na.rm=TRUE),
ci.degreeLostD = 1.96 * sd(degreeLostD,na.rm=TRUE)/sqrt(n()),
mean.avg_wealth = mean(avg_wealth,na.rm=TRUE),
ci.avg_wealth = 1.96 * sd(avg_wealth,na.rm=TRUE)/sqrt(n()),
mean.gini = mean(gini,na.rm=TRUE),
ci.gini = 1.96 * sd(gini,na.rm=TRUE)/sqrt(n())
)
)
kable(sum.netIntLowDegree[c(1:9)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,10:17)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,18:25)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,26:33)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,34:ncol(sum.netIntLowDegree))]) %>% kableExtra::kable_styling(font_size = 10)
compare_means(percentIsolation ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoop ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoopFinal ~ coopFrac, data=df.netIntLowDegree)
compare_means(nCommunities ~ coopFrac, data=df.netIntLowDegree)
compare_means(communitySize ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityInitial ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityFinal ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionRate ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLost ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostD ~ coopFrac, data=df.netIntLowDegree)
summary(lm(percentIsolation ~ assortativityInitial, data=df.netIntLowDegree))
#plot(df.netIntLowDegree$assortativityInitial, df.netIntLowDegree$percentIsolation)
#percentIsolation
g.percentIsolation = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolation", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation when defectors are assigned to 25% of nodes by degree, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.0990, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0022, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0024, xend = 3.2, yend = -0.0024), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0022, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,0.10))
print(g.percentIsolation)
#percentIsolationC
#percentage of isolation among those who cooperated in both practice rounds
g.percentIsolationC = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationC", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation within initial cooperators, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.0990, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0022, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0024, xend = 3.2, yend = -0.0024), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0022, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,0.10))
print(g.percentIsolationC)
#percentIsolationD
#percentage of isolation among those who defected at least once in practice rounds
g.percentIsolationD = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationD", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation within initial defectors, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=1, y=0.0990, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0022, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0024, xend = 3.2, yend = -0.0024), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0022, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,0.10))
print(g.percentIsolationD)
#avgCoopFinal
g.avgCoopFinal = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avgCoopFinal", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Cooperation in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Propoption of cooperators in final round") +
annotate("text", x=1, y=0.990, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0212, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0214, xend = 3.2, yend = -0.0214), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0212, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,1))
print(g.avgCoopFinal)
#avg_wealth
g.avg_wealth = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avg_wealth", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 6800, method="t.test", color="black") +
labs(
title = paste("Wealth in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Average wealth in final round") +
annotate("text", x=1, y=6900, label= "ref", color="black") +
annotate("text", x=1.1, y= -212, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -214, xend = 3.2, yend = -214), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -212, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,7000))
print(g.avg_wealth)
#gini
g.gini = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="gini", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.48, method="t.test", color="black") +
labs(
title = paste("Gini coefficient in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Gini coefficient in final round") +
annotate("text", x=1, y=0.490, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0112, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0114, xend = 3.2, yend = -0.0114), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0112, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,0.5))
print(g.gini)
#degree
g.degree = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="degree", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 14.8, method="t.test", color="black") +
labs(
title = paste("Degree in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Mean degree in final round") +
annotate("text", x=1, y=14.90, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.312, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.314, xend = 3.2, yend = -0.314), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.312, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,15))
print(g.degree)
#transitivity
g.transitivity = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="transitivity", add = "mean_se") +
stat_compare_means(ref.group = "0.25", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Transitivity in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Degree percentile of nodes assigned to defectors ",
y = "Transitivity in final round") +
annotate("text", x=1, y=0.99, label= "ref", color="black") +
annotate("text", x=1.1, y= -0.0212, label= "Lowest degree nodes assigned to defectors", size=2.5) +
geom_segment(aes(x = 1.8, y = -0.0214, xend = 3.2, yend = -0.0214), linewidth=0.2, arrow = arrow(length = unit(0.1, "cm"))) +
annotate("text", x=3.9, y= -0.0212, label= "Highest degree nodes assigned to defectors", size=2.5) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
coord_cartesian(ylim=c(0,1))
print(g.transitivity)
#initial C-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyC, y = percentIsolation)) +
geom_point() +
scale_x_continuous("C-assortativity") +
scale_y_continuous(paste("Proportion isolated, degree %ile = ",key,sep="")) +
coord_cartesian(ylim=c(0,0.2)) +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
#initial D-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyD, y = percentIsolation)) +
geom_point() +
scale_x_continuous("D-assortativity") +
scale_y_continuous(paste("Proportion isolated, degree %ile = ",key,sep="")) +
coord_cartesian(ylim=c(0,0.2)) +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
}
}
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Warning: package 'reldist' was built under R version 4.1.2
## reldist: Relative Distribution Methods
## Version 1.7-1 created on 2022-05-14.
## copyright (c) 2003, Mark S. Handcock, University of California-Los Angeles
## For citation information, type citation("reldist").
## Type help(package="reldist") to get started.
##
## Attaching package: 'reldist'
## The following object is masked from 'package:arsenal':
##
## iqr
##
## Attaching package: 'boot'
## The following object is masked from 'package:lattice':
##
## melanoma
## Warning: The dot-dot notation (`..p.signif..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(p.signif)` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
plot.trends <-
data.frame(
trends.df %>%
group_by(round, V, GINI, fractionCoop) %>%
summarize_all(list(mean=~mean(., na.rm=TRUE),sd=~sd(., na.rm=TRUE)))
)
plot.trends$V = factor(plot.trends$V)
plot.trends$GINI = factor(plot.trends$GINI)
for(i in unique(plot.trends$fractionCoop)){
g.gini = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gini_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gini_mean - gini_sd, ymax = gini_mean + gini_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gini") +
theme_bw()
g.gmd = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gmd_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gmd_mean - gmd_sd, ymax = gmd_mean + gmd_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gmd") +
theme_bw()
g.avg_wealth = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_wealth_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_wealth_mean - avg_wealth_sd, ymax = avg_wealth_mean + avg_wealth_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_wealth") +
theme_bw()
g.avg_coop = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_coop_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_coop_mean - avg_coop_sd, ymax = avg_coop_mean + avg_coop_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_coop") +
theme_bw()
g.avg_degree = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_degree_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_degree_mean - avg_degree_sd, ymax = avg_degree_mean + avg_degree_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_degree") +
theme_bw()
plot <- ggarrange(g.gini,g.gmd,g.avg_wealth,g.avg_coop,g.avg_degree,common.legend = TRUE,legend="bottom")
print(annotate_figure(plot, top = text_grob(paste("Degree percentile of nodes assigned to defectors =",i), color = "black", face = "bold", size = 10)))
}
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Warning: Removed 6 rows containing missing values (`geom_line()`).
